The Nature Reviews article is a review of a full article (listed below). Here;s the review (not sure why the capitals got lost):

restless legs syndrome (rls) is associatedwith Crohn’s disease according to theresults of a new study. the authors ofthis study suggest that rls could bean extraintestinal manifestation ofCrohn’s disease.

rls is a central nervous system (Cns)disorder characterized by an irresistibleurge to move the legs at rest—usually torelieve uncomfortable sensations.rls can be primary (idiopathic andfamilial) or secondary. “i decided to lookat the subject of secondary rls and seewhat kind of diseases and/or disordershave been associated with this condition,”says weinstock, lead author on the study.

272 consecutive patients withconfirmed Crohn’s disease completedwritten questionnaires to prospectivelyevaluate the presence of rls. Diagnosisof rls was made according to fourestablished criteria.

the incidence of rls (that is, at anypoint) in patients with Crohn’s disease was43%. the prevalence of rls (that is, at thetime of evaluation) was significantly higherin patients with Crohn’s disease comparedwith control patients (in this case spousesof patients with Crohn’s disease). rlsstarted during or after the onset of Crohn’sdisease in 91.8% of patients.

“our conclusion is that rls occursfrequently in patients with Crohn’sdisease—the prevalence of rls in patientswith Crohn’s disease is higher than formany of the previously known secondarycauses of rls,” says weinstock.

“i am in discussions with investigatorsat Johns Hopkins to see if inflammatorymarkers can be checked in patients withCrohn’s disease and rls before and aftertreatment for Crohn’s disease, to see ifthere is a correlation with resolution ofrls,” concludes weinstock.

Hmm.. This is along the lines of what I thought they were likely going to say they found..

That still leaves primary RLS in the dark as for an exact cause.. And for secondary, I'm not really sure if its fair in my mind to say Chrohn's causes RLS or RLS is a related "manifestation" of Chron's or anything like that.. Otherwise you might as well say Diabetes, Renal Failure, etc all cause RLS too.. When in reality the RLS is simply a symptomatic indicator, or causality of the real health issue.

Restless Leg Syndrome seems to have a stigma attached to it, thanks to names like the "Jimmy Legs," but three Brigham Young University students say the problem is serious for many Americans and needs a treatment that works.

Jared Edgel, Ryan Allred and Tim Lovell are the founders of TranquilMed, which is coming out with an infrared product called RestEasy to treat RLS. Their business plan for the new product edged out stiff competition to win the 2010 BYU Business Plan Competition, and the product should be on its way to clinical trials soon.

Edgel said RLS can often seem like a humorous problem because of the stigma. Though he suffers from a mild form of the syndrome himself, he said even he did not think a product to treat RLS could make a good business venture at first. When the group first began working on a business plan for an RLS treatment, Edgel said they made announcements in classes looking for people with RLS. In most cases, the class would laugh, and then one person would slowly raise his or her hand and say they had RLS.

Once the group began researching the syndrome, Edgel said it became clear how severe the cases can become for the 30 million people in the United States that deal with RLS. The syndrome causes an uncontrollable, painful urge to move and often is worst at night.

"The pain they're going through is incredible," he said.

One man in their research said he considered amputation as a viable option to stop the pain of RLS, Edgel said. Another said he kept a wooden stake by his bed to beat his legs to sleep at night.

The biggest problem, Edgel said, is that there are not many effective treatments available. There are medications, but some have serious side-effects like addictive and compulsive behaviors. In some cases, the drugs can lose their effectiveness and the symptoms can worsen.

Edgel said the idea for the infrared treatment came from a study at BYU by Rike Mitchell using the light to try and treat a different nerve disorder. Participants in the study found that the treatment actually helped to decrease their RLS symptoms significantly.

"We thank Dr. Rike Mitchell for her work," he said.

Allred said the infrared light penetrates tissues and helps increase blood flow. He said although Lovell and Edgel both suffer from RLS, the group did not realize just how prevalent it was until they began research. He said the fact that two of the business co-founders had RLS may have helped connect the business to people with RLS and understand the condition.

"It helps them, and it helps all of us to see that there is a need and to push it forward," he said.

TranquilMed edged out more than 20 competitors to take home the top prize of $50,000 in cash and services such as legal, accounting, consulting, logo and more. Allred said the team has been working on their business plan since November. Along with the assistance in starting the business from here, Allred said the competition was a boon to the business because it helped connect teams with advisory boards and people in the business field.

Allred said many people were willing to help the team with their business and help them learn how to run a business in the medical devices field. He said it was beneficial to figure out who to talk to and what processes to go through in creating a business. The competition also helped the team show people their product.

"It helps us establish some visibility and some exposure to our business plan," he said.

As winners of the competition, TranquilMed also earned a spot in the invitation-only Moot Corp business plan competition in Austin, Texas.

Rachel Christensen, program director of entrepreneurship in the Rollins Center, said students who take part in the BYU Business Plan Competition take part in several different competitions leading up to the final competition, where three judges choose a winner. The field was narrowed to three teams for the final round, in which Bazari took second place and FanFare took third. Winners often take part in other competitions around the country as well, she said.

"Our students always do well at external competitions, which brings positive recognition to the university," she said.

Restless legs syndrome (RLS) is one of the most common neurological disorders. Patients with RLS suffer from an urge to move the legs and unpleasant sensations located mostly deep in the calf. Symptoms mainly occur in resting situations in the evening or at night. As a consequence, initiation and maintenance of sleep become defective. Here, we performed a genome-wide association study to identify common genetic variants increasing the risk for disease. The genome-wide phase included 922 cases and 1,526 controls, and candidate SNPs were replicated in 3,935 cases and 5,754 controls, all of European ancestry. We identified two new RLS–associated loci: an intergenic region on chromosome 2p14 and a locus on 16q12.1 in a linkage disequilibrium block containing the 5′-end of TOX3 and the adjacent non-coding RNA BC034767. TOX3 has been implicated in the development of breast cancer. The physiologic role of TOX3 and BC034767 in the central nervous system and a possible involvement of these two genes in RLS pathogenesis remain to be established.

Introduction Top

Restless legs syndrome (RLS) is a common neurological disorder with a prevalence of up to 10 %, which increases with age [1]. Affected individuals suffer from an urge to move due to uncomfortable sensations in the lower limbs present in the evening or at night. The symptoms occur during rest and relaxation, with walking or moving the extremity leading to prompt relief. Consequently, initiation and maintenance of sleep become defective [1]. RLS has been associated with iron deficiency, and is pharmacologically responsive to dopaminergic substitution. Increased cardiovascular events, depression, and anxiety count among the known co-morbidities [1].

Genome-wide association studies (GWAs) identified genetic risk factors within MEIS1, BTBD9, PTPRD, and a locus encompassing MAP2K5 and SKOR1 [2]–[4]. To identify additional RLS susceptibility loci, we undertook an enlarged GWA in a German case-control population, followed by replication in independent case-control samples originating from Europe, the United States of America, and Canada. In doing so, we identified six RLS susceptibility loci with genome-wide significance in the joint analysis, two of them novel: an intergenic region on chromosome 2p14 and a locus on 16q12.1 in close proximity to TOX3 and the adjacent non-coding RNA BC034767.

Results/Discussion Top

We enlarged our previously reported [2], [4] GWA sample to 954 German RLS cases and 1,814 German population-based controls from the KORA-S3/F3 survey and genotyped them on Affymetrix 5.0 (cases) and 6.0 (controls) arrays. To correct for population stratification, as a first step, we performed a multidimensional scaling (MDS) analysis, leading to the exclusion of 18 controls as outliers. In a second step, we conducted a variance components analysis to identify any residual substructure in the remaining samples, resulting in an inflation factor λ of 1.025 (Figures S1 and S2). The first four axes of variation from the MDS analysis were included as covariates in the association analysis of the genome-wide stage and all P-values were corrected for the observed λ.

Prior to statistical analysis, genotyping data was subjected to extensive quality control. We excluded a total of 302 DNA samples due to a genotyping call rate <98 %. For individual SNP quality control, we adopted a stringent protocol in order to account for the complexity of an analysis combining 5.0 and 6.0 arrays. We excluded SNPs with a minor allele frequency (MAF) <5%, a callrate <98%, or a significant deviation from Hardy-Weinberg Equilibrium (HWE) in controls (P<0.00001). In addition, we dropped SNPs likely to be false-positive associations due to differential clustering between 5.0 and 6.0 arrays by adding a second set of cases of an unrelated phenotype and discarding SNPs showing association in this setup (see Materials and Methods). Finally, we tested 301,406 SNPs for association in 922 cases and 1,526 controls. Based on a threshold level of a nominal λ-corrected PGWA<10-4, a total of 47 SNPs distributed over 26 loci were selected for follow-up in the replication study (Figure 1, Table S1).
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Figure 1. Manhattan plot of the GWA.

Association results of the GWA stage. The x-axis represents genomic position along the 22 autosomes and the x-chromosome, the y-axis shows -log10(P) for each SNP assayed. SNPs with a nominal λ-corrected P<10−4 are highlighted as circles.
doi:10.1371/journal.pgen.1002171.g001

We genotyped these 47 SNPs together with 29 adjacent SNPs in strong linkage disequilibrium (LD, r2 = 0.5–0.9) using the Sequenom iPLEX platform in seven case-control populations of European descent, comprising a total of 3,935 cases and 5,754 controls. Eleven SNPs with a call rate <95%, MAF<5%, and P<0.00001 for deviation from HWE in controls as well as 432 samples with a genotyping call rate <90% were excluded. A set of 47 SNPs, genotyped in 186 samples on both platforms (Affymetrix and Sequenom), was used to calculate an average concordance rate of 99.24 %.

The combined analysis of all replication samples confirmed the known four susceptibility loci and, in addition, identified two novel association signals on chromosomes 2p14 and 16q12.1 (Table 1). To address possible population stratification within the combined replication sample, we performed a fixed-effects meta-analysis. For four of the replication case-control populations, we included λ inflation factors which were available from a genomic controls experiment in a previous study in these populations [4]. These were used to correct the estimates for the standard error. Joint analysis of GWA and all replication samples showed genome-wide significance for these two novel loci as well as for the known RLS loci in MEIS1, BTBD9, PTPRD, and MAP2K5/SKOR1 with a nominal λ -corrected PJOINT <5×10−8 (Table 1). Depending on the variable power to detect the effects, the separate analyses of individual subsamples in the replication either confirmed the association after correction for multiple testing or yielded nominally significant results (Tables S2 and S3). The differing relevance of the risk loci in the individual samples is illustrated in forest plots (Figure 2). There was no evidence of epistasis between any of the six risk loci (PBonferroni >0.45).
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Figure 2. Forest plots of the RLS risk loci (1 SNP per locus).

OR and corresponding confidence interval for the GWA sample, all individual replication samples, the combined replication sample as well as the combined GWA and replication sample are depicted. ORs are indicated by squares with the size of the square corresponding to the sample size for the individual populations. (A) rs2300478 in MEIS1; (B) rs9357271 in BTBD9; (C) rs1975197 in PTPRD; (D) rs12593813 in MAP2K5/SKOR1; (E) rs6747972 in intergenic region on chromosome 2; (F) rs3104767 in TOX3/BC034767.
doi:10.1371/journal.pgen.1002171.g002
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The association signal on 2p14 (rs6747972: nominal λ-corrected PJOINT = 9.03×10−11, odds ratio (OR) = 1.23) is located in an LD block of 120 kb within an intergenic region 1.3 Mb downstream of MEIS1 (Figure 3). Assuming a long-range regulatory function of the SNP-containing region, in silico analysis for clusters of highly conserved non-coding elements using the ANCORA browser (http://ancora.genereg.net) identified MEIS1 as well as ETAA1 as potential target genes [5], [6].
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Figure 3. New genome-wide significant RLS loci.

a) Risk locus on chromosome 2p14, showing the best-associated SNP rs6747972 and ±200 kb of surrounding sequence. b) Risk locus on chromosome 16p21, showing the best-associated SNP rs3104767 and ±200 kb of surrounding sequence. The left-hand x-axis shows the negative log10 of the nominal λ-corrected P-values of the GWA stage for all SNPs genotyped in the respective region. The right-hand x-axis shows the recombination frequency in cM/Mb. The y-axis shows the genomic position in Mb based on the hg18 assembly. The r2-based LD between SNPs is colour-coded, ranging from red (r2>0. to dark blue (r2<0.2) and uses the best-associated SNP as reference. This SNP is depicted as a violet diamond. Recombination frequency and r2 values are calculated from the HapMap II (release 22) CEU population. Plots were generated with LocusZoom 1.1 (http://csg.sph.umich.edu/locuszoom/).
doi:10.1371/journal.pgen.1002171.g003

The second locus on chromosome 16q12.1 (rs3104767: nominal λ-corrected PJOINT = 9.4×10−19, OR = 1.35) is located within an LD block of 140 kb (Figure 3), which contains the 5′UTR of TOX3 (synonyms TNRC9 and CAGF9) and the non-coding RNA BC034767 (synonym LOC643714). TOX3 is a member of the high mobility box group family of non-histone chromatin proteins which interacts with CREB and CBP and plays a critical role in mediating calcium-dependent transcription in neurons [7]. GWAs have identified susceptibility variants for breast cancer in the identical region [8]. The best-associated breast cancer SNP, rs3803662, is in low LD (r2~0.1, HapMap CEU data) with rs3104767, but showed association to RLS (λ-corrected nominal PGWA = 7.29×10−7). However, logistic regression analysis conditioned on rs3104767 demonstrated that this association is dependent on rs3104767 (rs3803662: PGWA/conditioned = 0.2883).

BC034767 is represented in GenBank by two identical mRNA transcripts, BC034767 and BC029912. According to the gene model information of the UCSC and Ensembl genome browsers (http://genome.ucsc.edu and http://www.ensembl.org/index.html), these mRNAs are predicted to be non-coding. Additional in silico analysis using the Coding Potential Calculator (http://cpc.cbi.pku.edu.cn) supported this by attributing only a weak coding potential to this RNA, suggesting a regulatory function instead [9]. We also searched for rare alleles with strong effects and performed a mutation screening by sequencing all coding and non-coding exons of TOX3 and BC034767 in 188 German RLS cases (Table S4). In TOX3, a total of nine variants not listed in dbSNP (Build 130) were found, three of which are non-synonymous. Only one of these is also annotated in the 1000 Genomes project (November 2010 data release). Three additional new variants were located in putative exons 1 and 2 of BC034767. Analysis of the frequency of these variants as well as all known non-synonymous, frameshift, and splice-site coding SNPs in TOX3 in a subset of one of the replication samples (726 cases and 735 controls from the GER1 sample) did not reveal any association to RLS. For a power of >80%, however, variants with an OR above 4.5 and a MAF ≥0.01 would be required. For even lower MAFs, ORs ≥10 would be necessary for sufficient power. Furthermore, the described CAG repeat within exon 7 of TOX3 was not polymorphic as shown by fragment analysis in 100 population-based controls.

According to publicly available expression data (http://genome.ucsc.edu), in humans, BC034767 is expressed in the testes only, while TOX3 expression has been shown in the salivary glands, the trachea, and in the CNS. Detailed in-depth real time PCR profiling of TOX3 showed high expression levels in the frontal and occipital cortex, the cerebellum, and the retina [10]. To assess a putative eQTL function of rs6747972 or rs3104767, we studied the SNP-genotype-dependent expression of TOX3 and BC034767 as well as of genes known to directly interact with TOX3 (CREB-1/CREBBP/CITED1) and potential target genes of long-range regulatory elements at the locus on chromosome 2 (MEIS1/ETAA1) in RNA expression microarray data from peripheral blood in 323 general population controls [11]. No differential genotype-dependent expression variation was found.

To assess the potential for genetic risk prediction, we split our GWA sample in a training and a test set and determined classifiers for case-control status in the training set to predict case-control status in the test set. Training and test set were independent of each other – not only with respect to included individuals but also with respect to the genotyping procedure as we used genotypes generated on different genotyping platforms. As training set, we used those cases of the current GWA which had been genotyped on 500K arrays in a previous GWA and the corresponding control set [2], in total, 326 cases and 1,498 controls. The test set comprised 583 cases and 1,526 controls, genotyped on 5.0/6.0 arrays as part of the current study. Prior to the analysis, we removed the six known risk loci and performed LD-pruning to limit the analysis to SNPs not in LD with each other. In the end, a total of 76,532 SNPs were included in the pruned dataset. We conducted logistic regression with age and sex as covariates. Based on these association results, the sum score of SNPs showing the most significant effects (i.e. the number of risk alleles over all SNPs) weighted by the ln(OR) of these effects was chosen as predictor variable in the test set. We then varied the P-value threshold for SNPs included in the sum score. For a P-value <0.6, we observed a maximum area under the curve (AUC) of 63.9% and an explained genetic variance of 6.6% (Nagelkerke's R), values comparable to estimates obtained for other complex diseases such as breast cancer or diabetes (Table S5) [12]–[14]. Inclusion of the six known risk loci in this analysis resulted in a maximum AUC of 64.2% and an explained genetic variance of 6.8%.

Additionally, we performed risk prediction in the combined GWA and replication sample including only the six established RLS risk loci. For this purpose, we used the weighted risk allele score resulting in ORs of up to 8.6 (95% CI: 2.46–46.25) and an AUC of 65.1% (Figures S3 and S4).

By increasing the size of our discovery sample, we have identified two new RLS susceptibility loci. The top six loci show effect sizes between 1.22 and 1.77 and risk allele frequencies between 19 and 82%, and reveal genes in neuronal transcription pathways not previously suspected to be involved in the disorder.
Materials and Methods Top
Study population and phenotype assessment
Ethics statement.

Written informed consent was obtained from each participant in the respective language. The study has been approved by the institutional review boards of the contributing authors. The primary review board was located in Munich, Bayerische Ärztekammer and Technische Universität München.
RLS patients (GWA and replication phase).

A total of 2,944 cases (GWA = 954, replication = 1,990) of European descent were recruited in two cycles via specialized outpatient clinics for RLS. German and Austrian cases for the GWA (GWA) and the replication sample (GER1) were recruited in Munich, Marburg, Kassel, Göttingen, Berlin (Germany, n in GWA = 830, n in GER1 = 1,028), Vienna, and Innsbruck (Austria, n in GWA = 124, n in GER1 = 288). The additional replication samples originated from Prag (Czech Republic (CZ), n = 351), Montpellier (France (FR), n = 182), and Turku (Finland (FIN), n = 141). In all patients, diagnosis was based upon the diagnostic criteria of the International RLS Study Group [1] as assessed in a personal interview conducted by an RLS expert. A positive family history was based on the report of at least one additional family member affected by RLS. We excluded patients with secondary RLS due to uremia, dialysis, or anemia due to iron deficiency. The presence of secondary RLS was determined by clinical interview, physical and neurological examination, blood chemistry, and nerve conduction studies whenever deemed clinically necessary.

In addition, 1,104 participants (GER2) of the “Course of RLS (COR-) Study”, a prospective cohort study on the natural course of disease in members of the German RLS patient organizations, were included as an additional replication sample. After providing informed consent, study participants sent their blood for DNA extraction to the Institute of Human Genetics, Munich, Germany. A limited validation of the RLS diagnosis among the majority of members was achieved through a diagnostic questionnaire. Five percent had also received a standardized physical examination and interview in one of the specialized RLS centers in Germany prior to recruitment. To avoid doublets, we checked these subjects against those recruited through other German RLS centers and excluded samples with identical birth date and sex.

556 cases (US) were recruited in the United States at Departments of Neurology at Universities in Baltimore, Miami, Houston, and Palo Alto. Diagnosis of RLS was made as mentioned above.

285 cases (CA) were recruited and diagnosed as above in Montréal, Canada. All subjects were exclusively of French-Canadian ancestry as defined by having four grandparents of French-Canadian origin.

Detailed demographic data of all samples are provided in Table S6.
Control populations (GWA and replication phase).

Controls for German and Austrian cases were of European descent and recruited from the KORA S3/F3 and S4 surveys, general population-based controls from southern Germany. KORA procedures and samples have been described [15]. For the GWA phase, we included 1,814 subjects from S3/F3, and, for the replication stage, 1,471 subjects from S4.

For replication of the GER2 sample, we used controls from the Dortmund Health Study (DHS), a population-based survey conducted in the city of Dortmund with the aim of determining the prevalence of chronic diseases and their risk factors in the general population. Sampling for the study was done randomly from the city's population register stratified by five-year age group and gender [16]. 597 subjects selected at random from the Czech blood and bone marrow donor registry served as Czech controls [17]. French controls included 768 parents of multiple sclerosis patients recruited from the French Group of Multiple Sclerosis Genetics Study (REFGENSEP) [18]. Finnish controls comprised 360 participants of the National FINRISK Study, a cross-sectional population survey on coronary risk factors collected every five years. The current study contains individuals recruited in 2002. Detailed description of the FINRISK cohorts can be found at www.nationalbiobanks.fi.

French-Canadian controls were 285 unrelated individuals recruited at the same hospital as the cases.

1,200 participants of the Wisconsin Sleep Cohort (WSC), an ongoing longitudinal study on the causes, consequences, and natural course of disease of sleep disorders, functioned as US controls [19].

None of the controls were phenotyped for RLS. All studies were approved by the institutional review boards in Germany, Austria, Czech Republic, France, Finland, the US, and Canada. Written informed consent was obtained from each participant. Detailed demographic data of all samples are provided in Table S6.
Genotyping
GWA.

Genotyping was performed on Affymetrix Genome-Wide Human SNP Arrays 5.0 (cases) and 6.0 (controls) following the manufacturer's protocol. The case sample included 628 cases from previous GWAs [2], [4] and 326 new cases. After genotype-calling using the BRLMM-P clustering algorithm [20], a total of 475,976 overlapping SNPs on both Affymetrix arrays were subjected to quality control. We added 655 cases of a different phenotype unrelated to RLS, genotyped on 5.0 arrays, to the analysis and excluded those SNPs which showed a significant difference of allele frequencies in cases (RLS and unrelated phenotype on 5.0) and controls (6.0) (n = 92). Thereby, we filtered out SNPs likely to be false-positive associations. We excluded SNPs with a minor allele frequency (MAF) <5% (n = 88,582), a callrate <98% (n = 65,906) or a significant deviation from Hardy-Weinberg Equilibrium (HWE) in controls (P<0.00001) (n = 20,060). Cluster plots of the GWA genotyping data for the best-associated SNPs in Table 1 are shown in Figure S5. Genotypes of these SNPs are available in Table S7.
Replication.

We selected all SNPs with a λ-corrected Pnominal<10−4 in the GWA for replication. These SNPs clustered in 26 loci (defined as the best associated SNP ±150 kb of flanking sequence). We genotyped a total of three SNPs in each of the 26 regions. These were either further associated neighbouring SNPs with a λ-corrected Pnominal<10−3 or, in case of singleton SNPs, additional neighbouring SNPs from HapMap with the highest possible r2 (at least >0.5) with the best-associated SNP. We also genotyped the best-associated SNPs identified in the previous GWAs [2], [4].

Genotyping was performed on the MassARRAY system using MALDI-TOF mass spectrometry with the iPLEX Gold chemistry (Sequenom Inc, San Diego, CA, USA). Primers were designed using AssayDesign 3.1.2.2 with iPLEX Gold default parameters. Automated genotype calling was done with SpectroTYPER 3.4. Genotype clustering was visually checked by an experienced evaluator.

SNPs with a call rate<95%, MAF<5%, and P<0.00001 for deviations from HWE in controls were excluded. DNA samples with a call rate<90% were also excluded.
Population stratification analysis
GWA.

To identify and correct for population stratification, we performed an MDS analysis as implemented in PLINK 1.07 (http://pngu.mgh.harvard.edu/~purcell/pli&#8203;nk, [21]) on the IBS matrix of our discovery sample. After excluding outliers by plotting the main axes of variation against each other, we performed logistic regression with age, sex, and the values of the MDS components as covariates. Using the Genomic Control approach [22], we obtained an inflation factor λ of 1.11.

Additionally, we performed a variance components analysis using the EMMAX software (http://genetics.cs.ucla.edu/emmax, [23]) and, again, calculated the inflation factor with Genomic Control, now resulting in a λ of 1.025. EMMAX uses a mixed linear model and does not only correct for population stratification but also for hidden relatedness. We, therefore, decided to base correction for population substructure on the EMMAX results.
Replication.

Correction for population stratification was performed for the German, Czech, and the Canadian subsamples. The λ-values of 1.1032, 1.2286, and 1.2637 were derived from a previous Genomic Control experiment within the same samples using 176 intergenic or intronic SNPs [4]. Here, we had applied the expanded Genomic Control method GCF developed by Devlin and Roeder [24]. In the meta-analysis of all replication samples, the λ-corrected standard errors were included for the German, Czech, and Canadian samples. For the other replication samples from France, Finland, and the USA, no such data was available and, therefore, no correction factor was included in the analysis.
Statistical analysis

Statistical analysis was performed using PLINK 1.07 (http://pngu.mgh.harvard.edu/~purcell/pli&#8203;nk, [21]). In the GWA sample, we applied logistic regression with age, sex, and the first four axes of variation resulting from an MDS analysis as covariates.

P-values were λ-corrected with the λ of 1.025 from the EMMAX analysis. In the individual analysis of the single replication samples, we tested for association using logistic regression and correcting for gender and age as well as for population stratification where possible (see Population Stratification). Each replication sample was Bonferroni-corrected using the number of SNPs which passed quality control for the respective sample.

For the combined analysis of all replication samples, we performed a fixed-effects inverse-variance meta-analysis. Where available, we used λ-corrected standard errors in this analysis. Bonferroni-correction was performed for 74 SNPs, i.e. the number of SNPs which passed quality control in at least one replication sample.

For the joint analysis of the GWA and the replication samples, we also used a fixed-effects inverse-variance meta-analysis and again included λ-corrected values as far as possible. For the conditioned analysis, the SNP to be conditioned on was included as an additional covariate in the logistic regression analysis as implemented in PLINK.

Interaction analysis was performed using the –epistasis option in PLINK. Significance was determined via Bonferroni-correction (i.e. 0.05/28, as 28 SNP combinations were tested for interaction).
Power calculation

Power calculation was performed using the CaTS power calculator [25] using a prevalence set of 0.08 and an additive genetic model (Table S3). The significance level was set at 0.05/74 for replication stage analysis and at 0.05/301,406 for genome-wide significance in the joint analysis of GWA and replication. For the rare variants association study, the significance level was set at 0.05/12.
Mutation screening of TOX3 and BC034767

All coding and non-coding exons including adjacent splice sites of TOX3 (reference sequence NM_001146188) and BC034767 (reference sequence IMAGE 5172237) were screened for mutations in 188 German RLS cases.

Mutation screening was performed with high resolution melting curve analysis using the LightScanner technology and standard protocols (IDAHO Technology Inc.). DNAs were analyzed in doublets. Samples with aberrant melting pattern were sequenced using BigDyeTerminator chemistry 3.1 (ABI) on an ABI 3730 sequencer. Sequence analysis was performed with the Staden package [26]. Primers were designed using ExonPrimer (http://ihg.gsf.de) or Primer3plus (www.bioinformatics.nl/cgi-bin/primer3pl ... r3plus.cgi). All identified variants were then genotyped in 735 RLS cases and 735 controls of the general population (KORA cohort) on the MassARRAY system, as described above.

In addition, fragment analysis of exon 7 of TOX3 was performed to screen for polymorphic CAG trinucleotide repeats. DNA of 100 controls (50 females, 50 males) was pooled and analyzed on an ABI 3730 sequencer with LIZ-500 (ABI) as a standard. Primers were designed using Primer3plus, the forward Primer contains FAM for detection. Analysis was performed using GeneMapper v3.5.
Expression analyses

Associations between MEIS1/ETAA1 RNA expression and rs6747972 and between TOX3/BC034767/CREB-1/CREBBP/CITED1 expression and rs3104767 were assessed using genome-wide SNP data (Affymetrix 6.0 chip) in conjunction with microarray data for human blood samples (n = 323 general population controls from the KORA cohort, Illumina Human WG6 v2 Expression BeadChip) [11]. A linear regression model conditioned on expression and controlling for age and sex was used to test for association.
Prediction of genetic risk
Based on the performance of P-value-threshold selected SNPs in a training and a test sample.

As training sample, we used those GWA-cases which had also been genotyped for our previous study [2]. We also included the control samples from this study. As a first quality control step, we carried out an association analysis comparing the Affymetrix 500K genotypes of these GWA-cases to the Affymetrix 5.0 genotypes of the same cases. Significant P-values would indicate systematic differences in the genotyping between the different chips. For further analysis, we only used those 259,302 SNPs with P-values >0.10. We performed a second quality control step in which IDs with a callrate below 98% and SNPs with a callrate below 98%, a MAF lower than 5%, or a P-value for deviation from HWE<0.00001 were removed.

Further, we excluded the four already known risk loci as well as the two newly identified loci and performed LD-pruning to limit the analysis to SNPs not in LD with each other. This was performed using a window-size of 50 SNPs. In each step, this window was shifted 5 SNPs. We used a threshold of 2 for the VIF (variance inflation factor). 76,532 SNPs, 326 cases, and 1,498 controls were included in the final training dataset. We conducted logistic regression with age and sex as covariates. Based on these association results, the sum score of SNPs showing the most significant effects (i.e. the number of risk alleles over all SNPs) weighted by the ln(OR) of these effects was chosen as predictor variable in the test set, comprising the remaining 583 cases of the GWA sample and 1,526 controls. None of these cases/controls were included in the training-sample, i.e. the test-sample constitutes a completely independent sample. Based on this sum score, we calculated the ROC curve and Nagelkerke's R to measure the explained variance.
Based on a weighted risk allele score.

To evaluate the predictive value in our sample, we calculated a weighted sum score of risk alleles in the combined GWA and replication sample. To this end, we used one SNP from each RLS risk region and also included markers from the two newly identified regions on chromosome 16q12 and 2p14 (MEIS1: rs2300478, 2p14: rs6747972, BTBD9: rs9296249, PTPRD: rs1975197, MAP2K5: rs11635424, TOX3/BC034767: rs3104767). At each SNP, the number of risk alleles was weighted with the corresponding ln(OR) for this SNP. The corresponding distribution of the score in cases and controls is illustrated in Figure S3. Employing this score for risk prediction resulted in an AUC of 0.651 (Figure S4).
Supporting Information Top

Figure S1.

MDS analysis plot for GWA. Distribution of cases (red) and controls (black) along the two main axes of variation identified in the MDS analysis. The three visible clouds are due to a common 3.8 Mb inversion polymorphism on chromosome 8 (described in: Tian C, Plenge RM, Ransom M, Lee A, Villoslada P, et al. (2008) Analysis and Application of European Genetic Substructure Using 300 K SNP Information. PLoS Genet 4: e4. doi:10.1371/journal.pgen.0040004).

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Figure S2.

QQ-plot of GWA results. QQ-plot showing the P-value distribution before (red) and after (blue) correction for population stratification using Genomic Control.

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Figure S3.

Weighted risk allele score analysis. Histogram of the weighted risk allele scores for cases and controls. The corresponding OR and CI for each category against the median category is depicted in green. The left y-axis refers to the number of individuals (in %), the right-axis refers to the OR values.

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Figure S4.

ROC curve for weighted risk score analysis. Receiver operating characteristic (ROC) curve for the weighted risk allele score approach of risk prediction. The area under the curve (AUC) is 65.1%.

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Figure S5.

Cluster plots of GWA genotyping for the six risk loci. For the best-associated SNPs at each risk locus, clusterplots were generated for cases and controls. Intensities of the A and B allele (based on the Affymetrix annotation of the SNPs) are given on the x- and y-axes and the respective genotypes are indicated in blue, green, and orange.

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Table S1.

GWA results for SNPs with λ-corrected PGWA<10–4 and additional SNPs selected for replication. A star (*) indicates SNPs which had been identified in previous RLS GWAs [2]–[4]. P-values of the GWA phase are given as λ-corrected nominal P-values. Two different methods for λ correction were applied, multi-dimensional-scaling (MDS)-analysis using PLINK and variance components (VC)-analysis using the EMMAX software with the P-values listed in the respective columns “MDS λ-corrected PGWA” and “VC λ-corrected PGWA”. The selection of SNPs for replication was based on the MDS λ-corrected P-values. r2-values based on Hapmap CEU data are given for those SNPs which were selected for replication based on their LD with the best-associated SNP in each region. Genomic position and gene annotation refer to the hg18 genome.

(DOC)

Table S2.

Replication stage association results for individual replication samples. P-values are derived from logistic regression and correcting for gender and age as well as for population stratification where possible (see Materials and Methods). Each replication sample was Bonferroni-corrected using the number of SNPs which passed quality control for the respective sample. The OR refers to the minor allele. NA; SNP could not be analysed due to failing quality control in the respective sample.

(DOC)

Table S3.

Power analysis for GWA, replication and joint analysis of GWA and replication. Power calculation was performed using the CaTS power calculator [25] using a prevalence set of 0.08 and an additive genetic model. The significance level α was set at 0.05/74 for replication stage analysis and at 0.05/301,406 for genome-wide significance in the joint analysis of GWA and replication.

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Table S4.

Results of TOX3 and BC034767 mutation screening. * “A” refers to the mutant allele, “B” to the reference allele. Position refers to hg18 genome annotation. Codon numbering refers to the reference sequence NM_001146188. Data of the 1000 genomes project was obtained from the November 2010 release via the 1000 genomes browser (http://browser.1000genomes.org/index.htm&#8203;l).

(DOC)

Table S5.

Prediction of genetic risk; training- and test-set approach. Inclusion threshold P-values were derived from a logistic regression with age and sex as covariates in the training sample. # SNPs indicates the number of SNPs passing the inclusion threshold. Based on these association results, the sum score of SNPs showing the most significant effects (i.e. the number of risk alleles over all SNPs) weighted by the ln(OR) of these effects was chosen as predictor variable in the test set. Based on this sum score, an AUC and Nagelkerke's R were calculated.

(DOC)

Table S6.

Demographic data of GWA and replication samples. Mean age, mean age of onset and respective standard deviations and ranges are given in years. N: number of individuals; SD: standard deviation; AAO: age of onset. GWA: Genome-wide association study; CZ: Czechia; FR: France; FIN: Finland; CA: Canada; US: United States. - indicates that this information is not applicable for the respective sample.

Study Medication and Dosage
The subjects were studied at night under 3 conditions: at baseline, after administration of melatonin, and during bright light exposure.

Outcome Measures
The severity of RLS symptoms was assessed by the suggested immobilization test (SIT), which allows quantification of both sensory and motor manifestations (SIT-PLM) of RLS.

Key Findings
There was a significant increase of SIT-PLM index when subjects took melatonin compared to both baseline and bright light conditions. Bright light exposure had no effect on leg movements but did produce a significant decrease in sensory symptoms. Exogenous melatonin may have a detrimental effect on motor symptoms, and bright light exposure produced small but significant improvement in leg discomfort.

Practice Implications
This small study may in time be seen as one of the first clinical trials that eventually led to a change in the way we approach restless leg syndrome (RLS) treatment in clinical practice.

Though it is rare for patients to present with RLS as their chief complaint, the condition is common, affecting 10–11% of the general population. Being female increases risk and so does the number of children a woman has had: Having 1 child nearly doubles the risk of having RLS, 2 children triples the risk, and 3 or more children increase the odds ratio to 3.57.1 It is surprising that more patients don’t complain about RLS.

RLS symptoms also follow a circadian pattern, being worse in the late evening at a time when presumably melatonin levels are either increasing or peaking.

Sir Thomas Willis first described RLS symptoms in 1685.2 Karl Axel Ekbom, a Swedish neurologist, is credited with writing the first modern clinical description and is also credited with naming the condition.3 The disease is actually being renamed; what we call restless leg syndrome will soon be known as Ekbom disease. “The main characteristics are the strong urge to move, accompanied or caused by uncomfortable, sometimes even distressing, paresthesia of the legs, described as a “creeping, tugging, pulling” feeling. The symptoms often become worse as the day progresses, leading to sleep disturbances or sleep deprivation, which leads to decreased alertness and daytime functions.”4

To comprehend the potential significance of this current paper by Whittom et al, we must review the current understanding of this disease.

Almost from the start, research on RLS has focused on iron deficiency, with Ekbom reporting an association in 1960.5 In 2005 Thorpy described RLS as an iron-deficiency symptom. This model explained the higher incidence of RLS in women.6

Over the last 7 years, several clinical trials have suggested that IV iron infusions will improve RLS symptoms. In an open label series, Earley started patients on single 1,000 mg doses of infused iron initially and then gave 450 mg if the patients' ferritin levels dropped below 300 mcg/l. These patients experienced benefit.7,8,9 An interim report on a placebo-controlled trial by Earley’s group at Johns Hopkins published in 2009 had not yet found measurable benefit at the time of writing.10 A different RCT published in 2009 by Swedish researchers did report that iron transfusions reduced RLS symptoms both in both short term and long term.11 We can probably say that iron infusions help some patients at least some of the time.

It is interesting—and counterintuitive—to note that blood donation does not worsen RLS symptoms.12

The relationship between iron and RLS is more complicated than a simple iron deficiency. In RLS the regulation of iron absorption and transport differs from that of healthy people. Earley reported that after the initial infusion, ferritin levels in his RLS subjects declined an average of 6.6 mcg/l/week, faster than the expected rate of <1 mcg/l per week. The faster the ferritin level dropped, the less likely the subsequent iron treatment was to provide benefit.13

Theories to explain RLS now center on iron levels in the brain. Conner et al have published a series of papers reporting on their analysis of brain samples obtained from RLS patients on autopsy. In 2003 their early data suggested that RLS results “from impaired iron acquisition by the neuromelanin cells. … The underlying mechanism may be a defect in regulation of the transferrin receptors.” In 2004 Connor et al reported in comparing further autopsy results that iron transport to the substantia nigra (SN) in RLS is different due to a defect in iron regulatory protein 1.14

The current data continue to suggest that RLS is a neurological disorder involving abnormal levels of iron in various parts of the brain. Mitochondrial ferritin (FtMt) levels correlate between iron levels and mitochondrial function in the SN. Autopsy samples from RLS patients have more FtMt than control samples. Neuromelanin-containing neurons in the SN were the predominant cell type expressing FtMt. These results suggest that increased numbers of mitochondria in neurons in RLS and increased FtMt might contribute to insufficient cytosolic iron levels in RLS SN neurons; these results are consistent with the hypothesis that energy insufficiency in these neurons may be involved in the pathogenesis of RLS.15

In 2011 Connor et al reported finding an iron regulatory protein that is decreased in RLS. They suggest that this leads to an alteration in iron management at the blood-brain interface. Thus there are “fundamental differences in brain iron acquisition in individuals with restless legs syndrome.” These researchers now identify a decrease in transferrin receptor expression in the microvasculature of the brains of RLS patients as that leads to demylination of neurons in the brain as possibly the underlying problem.16

Let us return to the current Whittom paper under analysis and see how it may relate to this understanding of how iron dysregulation underlies RLS.

Serum iron levels have long been thought to fluctuate according to a circadian rhythm, and the general consensus has been that they are higher in the morning, although not all data support this.17

RLS symptoms also follow a circadian pattern, being worse in the late evening at a time when presumably melatonin levels are either increasing or peaking.18 Working a rotating night shift, a practice that will likely disrupt melatonin production and circadian cycles, increases RLS symptom intensity.19 Micahud et al, who had looked for biochemical markers correlating with RLS symptoms, wrote in a 2004 report that “changes in melatonin secretion were the only ones that preceded the increase in sensory and motor symptoms in RLS patients. This result and those of other studies showing that melatonin exerts an inhibitory effect on central dopamine secretion suggest that melatonin might be implicated in the worsening of RLS symptoms in the evening and during the night.”20

A 2007 paper reported that melatonin changes the expression of transferrin receptors in the pineal glands of rats. These are the “membrane bound glycoproteins which function to mediate cellular uptake of iron from transferrin." Melatonin reduced transferring receptors.21 If melatonin can affect iron movement in the brains of rats, it would be a fair assumption that it may do the same in humans.

Two earlier papers looking at melatonin and RLS should be mentioned. Austrian researchers made the correlation that people with RLS often complain of insomnia and that melatonin may help insomnia, so they tested melatonin levels in individuals who have RLS hoping to find a deficiency. They did not. “Insomnia in RLS does not seem to be correlated with a deficit of melatonin.”22

An earlier paper published in 2001 will confuse the matter. Assuming that there was a deficiency of melatonin in RLS, German researchers gave 3 mg of melatonin to RLS patients for 6 weeks. They reported improved well-being and a reduction in limb movements in 7 of the 9 patients in their trial.23 Is there an explanation that will reconcile these contradictory results, or are we simply looking at an example of why the results of small open trials without control groups should be viewed with caution?

Let us assume for the moment that the results of both trials are true and that some unrevealed difference in protocol or patient selection accounts for the contrasting results. Then we might conclude that melatonin sometimes improves RLS symptoms and sometimes worsens them. In either event, moderating melatonin should be considered in patients with RLS.

It would make sense to find out how a particular patient will respond to melatonin supplementation as a first step in treating RLS. Depending on their reaction, it may be useful to encourage or discourage melatonin production. For example appropriate frequency lighting can be used to either increase or decrease melatonin production.

The relationship between ferritin and RLS is now well established, and levels should be monitored and corrected in all patients complaining of RLS.

One last bit of information is worth mentioning. An extract of the herb Salvia miltiorrhiza called Tanhinone II was recently reported to prevent brain iron dyshomeostasis. Though no published data as of yet appear in PubMed that suggest using these extracts to treat RLS, a number of proprietary herbal formulas do contain significant percentages of the herb and are sold specifically to treat RLS.24

Restless legs? There could be brain malfunction
Friday, November 18th, 2011

Researchers have found that people suffering from restless legs syndrome, which causes uncomfortable sensations in the limbs, have reduced function in an area of the brain important for controlling movement.

Preliminary results from a new study at Neuroscience Research Australia (NeuRA) suggest that such people have up to 80 percent less function in this brain region compared to healthy people.

“This is a disorder that is thought to affect one in 20 people, and can severely affect quality of life, but we still don’t know very much about it,” says Kay Double, associate professor of neuroscience.

“This study is helping us understand what happens in the brain to cause these symptoms, which will help us find better treatments,” she says, according to a NeuRA statement.

Restless legs syndrome is a disorder that causes uncomfortable sensations in the limbs.
It often flares up at night and disturbs sleep. It tends to run in families.

The NeuRA study is using ultrasound and magnetic resonance imaging (MRI) to look for changes in the structure and function of the brain.

“This is the first time that anyone has looked for these type of changes in people with restless legs syndrome,” says Double.

“If we can understand what is happening in the brain, we will be one step closer to helping the thousands with restless legs get a better night’s sleep and lead a better quality of life.”

But they said it is a mutation in the BTB9 (?) gene, so that would imply to me that they could test for variants of that gene in humans. However, it's not like other genetic conditions in that you wouldn't have children because of it or something, so even if they could easily test for it, I'm not sure the results would be helpful at all. I already know I have RLS, and that family members have it so it's likely genetic. Still doesn't help me sleep at night, although if the whole family were tested I would know whose name I should mutter whilst pacing at night.

Tracy

Opinions presented by Discussion Board Moderators are personal in nature and do not, in any way, represent the opinion of the WED/RLS Foundation, and are not medical advice.